In specific, the first kind of fusion methods rely on the manually designed rules to aggregate the modal feature approximately. These methods firstly utilize the auto encoder mechanisms to extract and reconstruct multimodal features to sufficiently learn the significant feature extraction. Then they develop various fusion rules for feature fusion, e.g., weighted average, summation, maximum selection, and \(\ell _{1}\)  norm. For instance, Li et al. [1]} pioneered the dense blocks as the learnable auto-encoder and utilized the weighted-average strategy to fuse modal features. After that, Li et al. [2]} also provide spatial/channel attention mechanisms as the fusion strategies to fuse features with nested connections. Subsequently, Liu et al. [3]} introduces the edge attention guided auto-encoder to extract feature and adopts the simple fusion rules.
